Computer Science > Neural and Evolutionary Computing

Title:Evolutionary Approach to Test Generation for Functional BIST

Abstract: In the paper, an evolutionary approach to test generation for functional BIST
is considered. The aim of the proposed scheme is to minimize the test data
volume by allowing the device's microprogram to test its logic, providing an
observation structure to the system, and generating appropriate test data for
the given architecture. Two methods of deriving a deterministic test set at
functional level are suggested. The first method is based on the classical
genetic algorithm with binary and arithmetic crossover and mutation operators.
The second one uses genetic programming, where test is represented as a
sequence of microoperations. In the latter case, we apply two-point crossover
based on exchanging test subsequences and mutation implemented as random
replacement of microoperations or operands. Experimental data of the program
realization showing the efficiency of the proposed methods are presented.